Identifying regions characterized by labeled measurements

ABSTRACT

Briefly, the disclosure describes embodiments of methods or apparatuses for processing, such as smoothing, a set of labeled measurements at a variety of scale levels. In one or more non-limiting embodiments purely for illustrative purposes, relatively fine details of labeled measurements may be displayed utilizing a relatively low-scale map, such as a map showing individual towns and/or villages. For display utilizing a relatively higher scale map, such as a map showing larger geopolitical areas, for example, relatively fine details may be omitted.

CROSS-REFERENCE TO RELATED APPLICATIONS

Although claimed subject matter is not necessarily limited in scope inthis respect, additional example embodiments of IDENTIFYING REGIONSCHARACTERIZED BY LABELED MEASUREMENTS may be discussed in concurrentlyfiled U.S. patent application Ser. No. 13/815,833, titled VISUALIZINGREGIONS CHARACTERIZED BY LABELED MEASUREMENTS, by Bart Thomee, et al,herein incorporated by reference in its entirety and assigned to theassignee of currently claimed subject matter and in concurrently filedU.S. patent application Ser. No. 13/844,637, (now U.S. Pat. No.9,058,666) titled FORMING REGIONS CHARACTERIZED BY LABELED MEASUREMENTSby Bart Thomee, et al. herein incorporated by reference in its entiretyand assigned to the assignee of currently claimed subject matter,application Ser. No. 13/844,760.

BACKGROUND

1. Field

This disclosure relates to processing labeled measurements usingsmoothing functions to create a visual representation of a regionidentified by the labeled measurements.

2. Information

In many applications, it may be useful to display measurements usingcomputer-assisted graphics to display measurements in a manner that maybe more easily interpreted by, for example, researchers, scientists,investigators, students, and/or others. For example, a medicalresearcher investigating the spread of an illness or disease across aregion, may find that computer-assisted images representing thegeographical distribution of the illness or disease may be a useful toolin determining locations at which disease-fighting resources may bepositioned. Such positioning may facilitate, for example, more efficientand/or effective deployment of healthcare assets in a manner that mayreduce incidence of cases of affliction as well as increase an abilityto treat those already afflicted.

In another example in which display of computer-assisted graphics may beuseful, a social sciences researcher may study measurements of labelsassociated with photographs in a database in an effort to study regionaland/or national trends. In one possible example, trends among certainage groups may be evaluated by studying types of photographs taken byindividuals of certain age groups and/or locations at which photographicimages are captured, for example. This research may be used, forexample, to uncover factors contributing to trends so that, for example,ramifications of elections, political and/or economic decisions, etc.,may be better understood. To facilitate investigation,computer-generated graphics, for example, may be used to assist aresearcher in visualizing trends and/or assessing how trends maypropagate within a society, region, nation, etc.

Unfortunately, contemporary computer visualization tool sets may not becapable of assisting in epidemiological or social sciencesinvestigations, for example. Thus, various types of epidemiology and/orsocial science research may be hindered by an inability tocomprehensively and/or accurately visualize various phenomena.Researchers, for example, may be constrained to using less-effectiveand/or manual tools, which may be slow and/or cumbersome to manipulate.Further, communication of results of investigations to an audience,perhaps by way of computer-assisted imagery, may be less effective oreven impaired. This may, for example, lessen the benefit of conductingthe investigation.

BRIEF DESCRIPTION OF DRAWINGS

Claimed subject matter is particularly pointed out and distinctlyclaimed in the concluding portion of the specification. However, both asto organization and/or method of operation, together with objects,features, and/or advantages thereof, claimed subject matter may beunderstood by reference to the following detailed description if readwith the accompanying drawings in which:

FIGS. 1A-1C are diagrams identifying one or more regions within ageographical area characterized by labeled measurements according to anembodiment;

FIG. 2 is a flow diagram of a process for identifying one or moreregions characterized by labeled measurements according to anembodiment;

FIG. 3 shows a disk-shaped structuring component as may be used incontour smoothing according to an embodiment;

FIGS. 4A and 4B show additional image processing that may be used toform one or more identified regions according to an embodiment; and

FIG. 5 is a schematic diagram of a system that may be employed foridentifying one or more regions characterized by labeled measurementsaccording to an embodiment.

Reference is made in the following detailed description to accompanyingdrawings, which form a part hereof, wherein like numerals may designatelike parts throughout to indicate corresponding and/or analogouscomponents. It will be appreciated that components illustrated in thefigures are not necessarily drawn to scale, such as for simplicityand/or clarity of illustration. For example, dimensions of somecomponents may be exaggerated relative to other components. Further, itis to be understood that other embodiments may be utilized. Furthermore,structural and/or other changes may be made without departing fromclaimed subject matter. It should also be noted that directions and/orreferences, for example, up, down, top, bottom, and so on, may be usedto facilitate discussion of drawings and/or are not intended to restrictapplication of claimed subject matter. Therefore, the following detaileddescription is not to be taken to limit claimed subject matter and/orequivalents.

DETAILED DESCRIPTION

In the following detailed description, numerous specific details are setforth to provide a thorough understanding of claimed subject matter. Forpurposes of explanation, specific numbers, systems, and/orconfigurations are set forth, for example. However, it should beapparent to one skilled in the relevant art having benefit of thisdisclosure that claimed subject matter may be practiced without specificdetails. In other instances, well-known features may be omitted and/orsimplified so as not to obscure claimed subject matter. While certainfeatures have been illustrated and/or described herein, manymodifications, substitutions, changes, and/or equivalents may occur tothose skilled in the art. It is, therefore, to be understood thatappended claims are intended to cover any and all modifications and/orchanges as fall within claimed subject matter.

Reference throughout this specification to one implementation, animplementation, one embodiment, an embodiment and/or the like may meanthat a particular feature, structure, or characteristic described inconnection with a particular implementation or embodiment may beincluded in at least one implementation or embodiment of claimed subjectmatter. Thus, appearances of such phrases, for example, in variousplaces throughout this specification are not necessarily intended torefer to the same implementation or to any one particular implementationdescribed. Furthermore, it is to be understood that particular features,structures, or characteristics described may be combined in various waysin one or more implementations. In general, of course, these and otherissues may vary with context. Therefore, particular context ofdescription or usage may provide helpful guidance regarding inferencesto be drawn.

Operations and/or processing, such as in association with networks, suchas communication networks, for example, may involve physicalmanipulations of physical quantities. Typically, although notnecessarily, these quantities may take the form of electrical and/ormagnetic signals capable of, for example, being stored, transferred,combined, processed, compared and/or otherwise manipulated. It hasproven convenient, at times, principally for reasons of common usage, torefer to these signals as bits, data, values, elements, symbols,characters, terms, numbers, numerals and/or the like. It should beunderstood, however, that all of these or similar terms are to beassociated with appropriate physical quantities and are intended tomerely be convenient labels.

Likewise, in this context, the terms “coupled”, “connected,” and/orsimilar terms, may be used. It should be understood that these terms arenot intended as synonyms. Rather, “connected” may be used to indicatethat two or more elements or other components, for example, are indirect physical and/or electrical contact; while, “coupled” may meanthat two or more components are in direct physical or electricalcontact; however, “coupled” may also mean that two or more componentsare not in direct contact, but may nonetheless co-operate or interact.The term coupled may also be understood to mean indirectly connected,for example, in an appropriate context.

The terms, “and”, “or”, “and/or” and/or similar terms, as used herein,may include a variety of meanings that also are expected to depend atleast in part upon the particular context in which such terms are used.Typically, “or” if used to associate a list, such as A, B or C, isintended to mean A, B, and C, here used in the inclusive sense, as wellas A, B or C, here used in the exclusive sense. In addition, the term“one or more” and/or similar terms may be used to describe any feature,structure, and/or characteristic in the singular and/or may be used todescribe a plurality or some other combination of features, structuresand/or characteristics. Though, it should be noted that this is merelyan illustrative example and claimed subject matter is not limited tothis example. Again, particular context of description or usage mayprovide helpful guidance regarding inferences to be drawn.

It should be understood that for ease of description a network devicemay be embodied and/or described in terms of a computing device.However, it should further be understood that this description should inno way be construed that claimed subject matter is limited to oneembodiment, such as a computing device or a network device, and,instead, may be embodied as a variety of devices or combinationsthereof, including, for example, one or more illustrative examples.

In this context, the term network device refers to any device capable ofcommunicating via and/or as part of a network. Network devices may becapable of sending and/or receiving signals (e.g., signal packets), suchas via a wired or wireless network, may be capable of performingarithmetic and/or logic operations, processing and/or storing signals,such as in memory as physical memory states, and/or may, for example,operate as a server. Network devices capable of operating as a server,or otherwise, may include, as examples, dedicated rack-mounted servers,desktop computers, laptop computers, set top boxes, tablets, netbooks,smart phones, integrated devices combining two or more features of theforegoing devices, the like or any combination thereof.

A network may comprise two or more network devices and/or may couplenetwork devices so that signal communications, such as in the form ofsignal packets, for example, may be exchanged, such as between a serverand a client device and/or other types of network devices, includingbetween wireless devices coupled via a wireless network, for example. Itis noted that the terms, server, server device, server-computing device,server-computing platform, and/or similar terms are usedinterchangeably. Similarly, the terms client, client device, clientcomputing device, client computing platform and/or similar terms arealso used interchangeably. While in some instances, for ease ofdescription, these terms may be used in the singular, such as byreferring to a “client device” or a “server device,” the description isintended to encompass one or more client devices or one or more serverdevices, as appropriate. Along similar lines, references to a “database”are understood to mean, one or more databases and/or portions thereof,as appropriate.

A network may also include now known, or to be later developedarrangements, derivatives, and/or improvements, including, for example,past, present and/or future mass storage, such as network attachedstorage (NAS), a storage area network (SAN), and/or other forms ofcomputer and/or machine readable media, for example. A network mayinclude the Internet, one or more local area networks (LANs), one ormore wide area networks (WANs), wire-line type connections, wirelesstype connections, other connections, and/or any combination thereof.Thus, a network may be worldwide in scope and/or extent. Likewise,sub-networks, such as may employ differing architectures or may becompliant and/or compatible with differing protocols, such ascommunication protocols (e.g., network communication protocols), mayinteroperate within a larger network. Various types of devices may bemade available so that device interoperability is enabled and/or, in atleast some instances, may be transparent to the devices. In thiscontext, the term transparent refers to communicating in a manner sothat communications may pass through intermediaries, but without thecommunications necessarily specifying one or more intermediaries, suchas intermediate devices, and/or may include communicating as ifintermediaries, such as intermediate devices, are not necessarilyinvolved. For example, a router may provide a link between otherwiseseparate and/or independent LANs. In this context, a private networkrefers to a particular, limited set of network devices able tocommunicate with other network devices in the particular, limited set,such as via signal packet transmissions, for example, without a need forre-routing and/or redirecting such communications. A private network maycomprise a stand-alone network; however, a private network may alsocomprise a subset of a larger network, such as, for example, withoutlimitation, the Internet. Thus, for example, a private network “in thecloud” may refer to a private network that comprises a subset of theInternet, for example. Although signal packet transmissions may employintermediate devices to exchange signal packet transmissions, thoseintermediate devices may not necessarily be included in the privatenetwork by not being a source or destination for one or more signalpacket transmissions, for example. As another example, a logicalbroadcast domain may comprise an example of a private network. It isunderstood in this context that a private network may provide outgoingcommunications to devices not in the private network, but such devicesoutside the private network may not direct inbound communications todevices included in the private network.

The Internet refers to a decentralized global network of interoperablenetworks, including devices that are part of those interoperablenetworks. The Internet includes local area networks (LANs), wide areanetworks (WANs), wireless networks, and/or long haul public networksthat, for example, may allow signal packets to be communicated betweenLANs. The term world wide web (WWW) and/or similar terms may also beused to refer to the Internet. Signal packets, also referred to assignal packet transmissions, may be communicated between nodes of anetwork, where a node may comprise one or more network devices, forexample. As an illustrative example, but without limitation, a node maycomprise one or more sites employing a local network address. Likewise adevice, such as a network device, may be associated with that node. Asignal packet may, for example, be communicated via a communicationchannel or a communication path comprising the Internet, from a site viaan access node coupled to the Internet. Likewise, a signal packet may beforwarded via network nodes to a target site coupled to a local network,for example. A signal packet communicated via the Internet, for example,may be routed via a path comprising one or more gateways, servers, etc.that may, for example, route a signal packet in accordance with a targetaddress and availability of a network path of network nodes to a targetaddress.

Physically connecting portions of a network via a hardware bridge, asone example, may be done, although other approaches also exist. Ahardware bridge, however, may not typically include a capability ofinteroperability via higher levels of a network protocol. A networkprotocol refers to a set of signaling conventions for communicationsbetween or among devices in a network, typically network devices, butmay include computing devices, as previously discussed; for example,devices that substantially comply with the protocol or that aresubstantially compatible with the protocol. In this context, the term“between” and/or similar terms are understood to include “among” ifappropriate for the particular usage. Likewise, in this context, theterms “compatible with”, “comply with” and/or similar terms areunderstood to include substantial compliance or substantialcompatibility. Typically, a network protocol has several layers. Theselayers may be referred to here as a communication stack. Various typesof communications may occur across various layers. For example, as onemoves higher in a communication stack, additional functions may beavailable by transmitting communications that are compatible and/orcompliant with a particular network protocol at these higher layers.

In contrast, a virtual private network (VPN) may enable a remote deviceto communicate via a local network. A router may allow communications inthe form of transmissions (e.g., signal packets), for example, to occurfrom a remote device to a VPN server on a local network. A remote devicemay be authenticated and a VPN server, for example, may create a specialroute between a local network and the remote device through anintervening router.

A network may be very large, such as comprising thousands of nodes,millions of nodes, billions of nodes, or more, as examples. Medianetworks, such as the Yahoo!™ network, for example, are increasinglyseeking ways to keep users within their networks by providing featuresand/or tool sets that users may find useful. A media network may, forexample, comprise an Internet website or group of websites having one ormore sections appealing to different interests or aspects of a user'sexperience, for example. For instance, the Yahoo!™ network includeswebsites located within different categorized sections, such as sports,finance, photo sharing (e.g., Flickr™) current events, and/or games, toname just a few among possible non-limiting examples.

The more users remaining within a media network for an extended period,the more valuable a network may become to potential advertisers. Thus,the more money advertisers may be inclined to pay to advertise to users,for example, via that media network. In an implementation, for example,an online photo sharing site, such as the Yahoo!™ Flickr™ service, mayenable a user to share photographs among a vast community of contactsand/or other users, via a server or other type of computing platform. Inparticular implementations, photographic assets, such as pictures, videoclips, multimedia clips, or other images, may be associated with one ormore labels, such as assigned by a user, so that other users of anonline service may access an image or other photographic asset ofpotential interest. Use of online photo sharing services may enable auser to keep up with friends and/or acquaintances, explore portions ofthe world with which they may be unfamiliar, and/or investigatelocalized areas, regions, and/or entire countries for future travel,just to name a few non-limiting examples. For example, if a user isinterested in traveling to Italy, a user may search an online photosharing service for images having a label, such as “Italy,” or may beassociated with locations, such as within the Italian peninsula.Although claimed subject matter is, of course, not limited in thisrespect, an implementation, such as online photo sharing services, mayentice users to remain within a network for a relatively extendedperiod.

In an implementation, a media network may serve as a collection pointfor “labeled measurements” that may be associated with one or morelocations, such as a geographical location, for example. However,labeled measurements need not be restricted to points that representphysical locations on the surface of the Earth, for example. In otherimplementations, a measurement may be associated with a non-spatialphysical manifestation, that may, for example, be represent as one ormore coordinate points, such as in a multidimensional vector space, asignal space, or another domain that may be employed to represent one ormore features of a measurable physical manifestation. For example, afrequency domain or a wavelength domain as simply one example, may beemployed. Within a given space, points, lines, and/or surfaces may berepresented using a linear combination of a set of mutually orthogonalvectors that span the space, although, again, this is simply anon-limiting example. It should be noted that claimed subject matter isintended to embrace all domains capable of representing a measurablephysical manifestation, whether in two dimensions, three dimensions,and/or additional dimensions.

In an implementation, as another example, a labeled measurement maycomprise one or more physical states (e.g., memory states) to encodelight levels of various wavelengths in an image capture device (e.g.,camera). Thus, for example, a labeled measurement may represent an imagecaptured and stored as a JPEG, TIFF, or other type of physical state andmay be associated with an approximate location on the Earth at which animage was captured. Accordingly, a labeled measurement may include aphotograph, video clip, and/or other multimedia segment, just to name afew examples. Of course, again, claimed subject matter is not limited toillustrative examples.

In another implementation example, a labeled measurement may comprise anarrangement of one or more physical states (e.g., memory states) toencode, for example, a date and/or a location of a meeting, a dateand/or location of a social event, such as a parade, a party, or otherfestivity, etc. A labeled measurement may comprise a report of adiscrete event, such as a measurement of a snow level at a location at acertain time or a location and time of an incidence of a disease orillness, just to name a few examples. A labeled measurement may beassociated with a location larger than a single point, such as arelatively small area, such as 1.0 square kilometer or less, or may beassociated with events distributed over a much larger region, such as acountry, continent, or other landmass. Again, claimed subject matter isnot limited to illustrative example. Thus, a labeled measurement mayalso represent a summation or other aggregation of measurements, suchas, for example, an indication of snowfall measurements extracted fromvarious points located on mountain, an entire mountain resort, or acrossa mountainous region. Accordingly, claimed subject matter is intended toembrace identifying labeled measurements comprising any number ofarrangements of physical states, for example, that may encodemeasurements that may be useful and/or of interest.

According to one or more implementations, as discussed herein, labeledmeasurements may be assigned a label corresponding to a name of a city,such as San Francisco, Los Angeles, Madrid, or the like. A label mayalso comprise a name of an establishment, such as the Golden GateBridge, Fenway Park, and so forth; a name of a naturally occurringfeature, such as the Grand Canyon, Yellowstone National Park, and soforth; or may include any other identifier. Labels may also comprise anyother descriptive references, such as clouds, sunsets, beaches, parks,countries, names of individuals or groups, and/or a large variety ofother descriptors that may be useful in categorizing a labeledmeasurement. It should be noted that claimed subject matter iscontemplated as embracing all types of labels that may be assigned tomeasurements and all types of physical locations or abstracted locationswithin mathematical or vector spaces associated with such measurements,for example.

In an implementation, a computing device, for example, may receiveand/or access labeled measurements stored in memory. In certainimplementations, a computing device, for example, may construct atwo-dimensional histogram comprising a number of bins, which maycorrespond to, for example, an area encompassed by 0.01° latitude and/or0.01° longitude on the surface of the Earth. In other implementations,smaller discretizations of latitude and/or longitude other than 0.01°are possible as well as larger discretizations, such as 0.02°. Again,these are illustrations and claimed subject matter is not intended to belimited to illustrations. Therefore, other discretizations may beemployed. Thus, in one possible example, of which many examples arepossible, labeled measurements of a particular bin may be assigned orassociated with corresponding label(s) nearest (e.g., within) 0.01°latitude and/or 0.01° longitude on the surface of the Earth. In otherpossible examples, discretizations pertaining to non-georeferencedlabeled measurement bins may correspond, for example, to dimensionsmeasured in nanometers, kilometers, miles, acres, and so forth.Non-georeferenced labeled measurements may correspond to dimensionsmeasured in light years, for example, for extraterrestrial measurements.Discretizations may pertain to a number of pixels in an image, forexample. Discretizations may pertain to un-evenly spaced dimensions atleast partially resulting from non-linear conversions from labeledmeasurements (e.g. self-organizing feature maps trained usingunsupervised learning). Again, these represent only a few examples of amyriad of possible examples, and claimed subject matter is not limitedin this respect.

In an implementation, a representation of at least one identified regionencompassing locations of measured labels may be exhibited using one ormore “layers” on a display. It is noted that the term layer may beassociated with a particular scale level out of a variety of possiblescale levels. At a given layer, which may represent, for example, arelatively high-resolution representation of a portion of the surface ofthe earth, a region may be identified by detecting one or more outermostiso-contours that may bound an area within which prominence orconformance of label measurements relative to other label measurementsmay drop below a threshold level, for example. In this context, the termprominence refers to a characteristic in which some label measurementsmay tend stand out among a larger set of label measurements. In someimplementations, a weighting function that may be applied to labeledmeasurements to enhance a contribution (e.g. prominence) of some labeledmeasurements in comparison other labeled measurements Likewise, in thiscontext, the term conformance refers to a characteristic in which somelabel measurements may tend to be consistent within a larger set oflabel measurements. To display an identified region covering a largerarea, for example, representing a portion of the surface of the Earth atsomewhat lower resolution, e.g., another layer or different scale level,a computing device may generate a second histogram. A second histogrammay, at least in part, permit a user to increase or to decrease a zoomlevel in a reasonably consistent manner between a lower resolution layer(e.g., relatively higher scale level) encompassing a relatively largeidentified region and a higher resolution layer (e.g., relatively lowerscale level) encompassing a smaller identified region, for example.

In an implementation, a second layer may be generated using a smoothingprocess, such as with respect to the previously constructed histogram. Asmoothing process may comprise, at least in some implementations, aconvolution of a two-dimensional Gaussian kernel with the previouslyconstructed histogram, although other smoothing approaches may beemployed. Again, claimed subject matter is not limited to illustrations.

In certain implementations, a set of labeled measurements at a varietyof scale levels may be smoothed in a manner that is at leastapproximately consistent with measurement labels across a variety ofscale levels. For example, at a relatively large scale, if a mapcorresponding to the European continent is displayed, regions that maybe identified by way of “country-level” features, such as where Spanishor German citizens are currently located, may be smoothed using arelatively large Gaussian kernel, for example, and displayed. In asomewhat smaller relative scale, encompassing, for example, a singlecountry, (e.g., Spain), regions that may be identified by areas within acountry, such as the Spanish regions of Andalusia, Extremadura, orValencia may be smoothed using a somewhat relatively smaller Gaussiankernel, for example. At still other scale levels, such as at a city ortown level, identified regions that may be characterized by labelscorresponding to a town square, a mall, or other location within a townmay be smoothed using a somewhat relatively smaller Gaussian kernel, forexample, and displayed. Claimed subject matter is intended to embraceall instances in which labels associated with measurements may besmoothed in a manner appropriate or consistent with scale levels. Inthis context, as suggested previously, a relatively high resolutionrefers to a relatively lower scale and a relatively lower resolutionrefers to a relatively higher scale.

In accordance with some embodiments, a computing device may filtermeasurements having labels occurring with less prominence or conformancethan other labels. Filtering, which may be implemented differently fordifferent scale levels, as described further herein, may permit displayand/or visualization of a level of detail appropriate for a particularlayer. In one possible example, a researcher may wish to view anepidemiological map on a national scale. To facilitate viewing at arelatively higher scale level, for example, details that may be usefulat a relatively lower level scale level, such as details at a town orvillage level, for example, may be merged by way of spatial filteringand/or one or more image processing techniques, so that nuances oflarger scale phenomena (e.g., intrusion of an epidemic into neighboringcountries) that may be of interest may be presented more effectively.

In a particular implementation, a Gaussian smoothing operation may beemployed at levels other than a relative high resolution. For example,in an embodiment, measurements providing a highest resolution for aparticular set of available measurements may be referred to as a‘zeroth’ layer or level. At various subsequent layers, in an embodiment,a Gaussian smoothing function may comprise a kernel, which may, forexample, be scaled proportional to a square root of a layer numericalvalue if the layer value comprises a nonzero value. In someimplementations, while smoothing a first layer, a scale factor “a” maybe used, wherein a corresponds to a standard deviation of the particularGaussian kernel at the particular level or layer. Accordingly, at afirst layer, a may be scaled to approximately equal 1.0. At a secondlayer, a may be scaled to approximately equal to the square root of 2.0,or approximately 1.414. Scaling of a to successively higher values toperform smoothing operations at layers other than the zeroth layer may,at least in some implementations, result in fine scale features beingreduced, such as at least approximately monotonically, as subsequentlayers (e.g., first, second, third, and so forth) are constructed. Forexample, in an embodiment, roughly equivalent or consistent intervalsmay be employed between successive layers or levels. Although Gaussiansmoothing may be contemplated for at least some implementations, asindicated, other types of smoothing approaches, such as binomial,Savitsky-Golay, and so forth, may be utilized and are included withinclaimed subject matter. However, use of other types of smoothingapproaches may result, at least in part, in an introduction of finescale features (artifacts) as a smoothing kernel comprises a largervalue, for example.

FIGS. 1A-1C are diagrams showing regions within a geographical areaidentified by labeled measurements according to an embodiment. In FIG.1A, map 10 represents the North American continent comprising identifiedregions 100, 150, and 175, wherein a scale of 1.0 cm representsapproximately 1000.0 km. Identified region 100 may correspond to, forexample, the San Joaquin Valley in California of the western UnitedStates. Identified region 150 may correspond to, for example, a portionof the Sonoran Desert in the southwest portion of the United States.Identified region 175 may correspond to, for example, an area of thecentral plains of the United States.

In one non-limiting example, identified regions 100, 150, and 175 mayencompass locations within which a weather event, such as flooding,drought, etc., may be occurring. Accordingly, for the case of a drought,for example, measurements, such as reports of river levels at a giventime, underground aquifer levels, mean daytime temperatures at differentlocations and times, and/or other indicators from locations withinidentified regions 100, 150, and 175 may be assigned a labelcorresponding to “drought,” for example. However, claimed subject matteris, of course, not limited to the use of particular geographicidentified regions, particular weather occurrences within identifiedregions, particular times, or particular labels assigned tomeasurements, which are provided as illustrations.

In FIG. 1B, a map 11 showing identified region 100′, which maycorrespond, for example, to the San Joaquin Valley in California, may beseen with greater detail than identified region 100 of FIG. 1A. In FIG.1B, 1.0 cm represents approximately 300.0 km. Identified regions 112,114, 116, and 118, which are not visible in identified region 100 ofFIG. 1, are visible in FIG. 1B. For the example of FIG. 1B, measurementsreported from locations within identified regions 112, 114, 116, and 118may, for example, describe river levels, underground aquifer levels,and/or other indicators. Labeled measurements associated with locationswithin these identified regions may be assigned a label corresponding to“severe drought,” for example.

In FIG. 1C, identified regions 112′, 114′, 116′, and 118′ of a map 12are visible, for example. In FIG. 1C, 1.0 cm represents approximately150.0 km. Within identified region 112′ of FIG. 1C, identified region113 is visible. Within identified region 114′, identified region 115 isvisible. Within identified region 116′, identified region 117 isvisible. Within identified region 118′, identified region 119 isvisible. For the example of FIG. 1C, measurements from locations withinidentified regions 113, 115, 117, and 119 may, for example, describeriver levels, underground aquifer levels, and/or other indicators.Measurement labels corresponding to locations from within theseidentified regions may be assigned a label corresponding to “extremedrought,” for example.

From FIGS. 1A, 1B, and 1C and as described herein, visible regionsidentified by “drought,” “severe drought,” and/or “extreme drought,” forexample, for example, may be displayed at a level of resolutionconsistent with a given scale and/or zoom command from an interface,such as a graphical user interface (GUI). Additionally, identifiedregions 100, 100′, and 100″ are shown with increasing detail asresolution increases. For example, identified region 100, as shown inFIG. 1A, may appear to be smoothed in relation to identified region 100′of FIG. 1B. Additionally, identified region 100′ of FIG. 1B may appearto be smoothed in relation to identified region 100″ of FIG. 1C.Likewise, identified regions 112, 114, 116, and 118 of FIG. 1B mayappear smoothed in relation to identified regions 112′, 114′, 116′, and118′, of FIG. 1C. In the example of FIGS. 1A-1, a level of detailappropriate for the particular layer (e.g., 1.0 cm=1000.0 km, forexample) may be achieved by smoothing a relatively higher-resolutionlayer (e.g. 1.0 cm equals 300.0 km), for example.

One or more identified regions displayed in greater detail in arelatively higher resolution layer need not be contiguous to bedisplayed, as such for a lower resolution layer. For example, in FIG.1C, identified region 126 is shown as detached from identified region116′. Similarly, identified region 127 is shown as detached fromidentified region 100″. However, identified regions 126 and 127 may beincorporated into larger immediately adjacent regions in FIG. 1B, forexample. These examples illustrate, for an embodiment, use of one ormore image processing techniques, which may include morphologicaltransforms, such as contour tracing, closing, filling, dilation,erosion, or combinations thereof. Image processing techniques may beemployed with relatively higher resolution regions in order to displaycorresponding regions at relatively lower resolutions using anappropriate level of detail, in an embodiment.

FIG. 2 is a flow diagram of a process for identifying a regioncharacterized by labeled measurements according to an embodiment 20. InFIG. 2, labeled measurements in the form of captured still images ofpopular attractions in New York City are shown as comprising inputsignals to process embodiment 20. Although three captured images (210,215, and 220) are shown, implementations may make use of hundreds,thousands, millions or even more labeled captured images. Further,labeled measurements representing event notices, reports, video clips,audio clips, multimedia segments, and so forth, for example, may also berepresented by input signals to process embodiment 20. That is, capturedimages are provided as a non-limiting example illustration.

Captured images 210, 215, and 220 have been assigned Label_1 andLabel_2. Captured images 210, 215, and 220 may be assigned additionallabels, which may correspond to a name of an individual capturing theimage, a name corresponding to the attraction, (e.g., Chrysler building,United Nations building, Statue of Liberty, and so forth), name of anevent, and/or any other label, as examples, and claimed subject matteris not limited in this respect. For the purposes of discussion of FIG.2, Label_1 may be assumed to be text, such as “New York” or, forexample, a slightly misspelled version, such as “Niew York.”

As shown in FIG. 2, captured images 210, 215, and 220 may additionallybe associated with a measurement corresponding to, for example, alocation at which the image was captured. For the example of FIG. 2,location may also correspond to latitude and/or longitude at which aphotographic image was captured. However, locations may be expressed inother forms, such as street addresses, well-known establishments, suchas Yankee Stadium, or any other technique of expressing a location in areasonably unambiguous manner, and claimed subject matter is not limitedin this respect.

Process embodiment 20 of FIG. 2 begins at 225, wherein normalization oflabels assigned to captured images may take place. For example, a slightmisspelling of “New York,” such as as “Niew York,” may be corrected. Inimplementations, normalization may comprise other operations such astranslation from one language to another, as may be encountered if oneor more captured images is assigned a label, for example, “Park” (inEnglish), “Parc” (in French), and “Parque” (in Spanish). In certainimplementations, normalization may also involve removal of diacriticalmarks, for example, present in a first language but not present in asecond language. For example, measurements assigned a labelcorresponding to “España” (in Spanish) may be normalized to “Spain” inEnglish. It should be noted that many types of translations from a firstlanguage to a second language are possible, as well as transliterationfrom a first character set to a second character set may be possible ofbeing performed at 225, and claimed subject matter is intended toembrace all such occurrences or embodiments.

Normalization at 225 may also be expressed in mathematical terms. Inparticular implementations, given a set of labels, denoted as “Λ,” ameasurement collection may be described as D_(Λ)=∪ of D|λεΛ, wherein the“∪” symbol indicates a union of various labeled measurements, such asassociated with a label D_(λ), for example. In an implementation,measurements associated with multiple labels, such as Label_1, Label_2,and so forth, may be included as many times in D_(Λ) for which aparticular asset has labels. For example, if a measurement is assigned15 labels (e.g. Λ□15), for example, a possibility for 15 labelnormalization operations may be performed. In an embodiment, it may benoted that any number of labels may be assigned to a measurement, andclaimed subject matter is not limited in this respect. As it pertains toa location associated with captured images, such as 210, 215, and 220,there is further described as D_(λ)

{d}, in which “d” comprises a label “λ” and may be represented by anordered list of elements, such as (l_(h), l_(w)) which may contain ageographic location expressed by longitude l_(h), and latitude l_(w).

Scale-space theory, for example, which refers to a framework formulti-scale signal representation, may be employed in processing labeledmeasurements associated with a location. Thus, at 230, a histogram of ahorizontal dimension “w” and of a vertical dimension “h” may beconstructed. In one possible example, a discretized histogram may beexpressed, for a label, such as Label_1, to captured images:

$\begin{matrix}{{D_{\lambda}} = {\sum\limits_{x = 1}^{w}\;{\sum\limits_{y = 1}^{h}\;{f_{\lambda}\left( {w,h} \right)}}}} & (1)\end{matrix}$wherein, expression 1 expresses D_(λ), for Label_1, Label_2, and soforth, producing in this example a two-dimensional density histogramf_(y)(w,h). In at least one implementation, locations associated withlabeled measurements may be represented using discrete increments oflongitude and latitude. Accordingly, for increments of 0.01 degreeslatitude, 36,000 discrete increments (w) are possible(360°/0.01°=36,000). Additionally, for increment sizes of 0.01 degreeslongitude, 18,000 discrete increments (h) are possible(180°/0.01°=18,000). In the histogram illustrated at 230, grid pointsare shown at intersections of lines of constant “h” and lines ofconstant “w,” for example.

At grid points of a histogram, such as illustrated at 230, which maynumber 36,000 (approximately) in a horizontal direction (w) and maynumber 18,000 (approximately) in a vertical direction (h), a linearscale-space representation of a family of increasingly smooth histogramsL_(λ)(w,h;t) for a given label (λ) may be described or computed as aconvolution of f_(λ)(w,h) with Gaussian kernels represented by G(w,h;t)may be described substantially in accord with expression 2, below as:L _(λ)(w,h;t)=G(w,h;t)*f _(λ)(w,h)  (2)wherein, t denotes variance of a kernel, and operator “*” indicates aconvolution operation. Process embodiment 20 may thus continue at 235wherein kernel operations may be calculated and/or scaled so thatGaussian smoothing may be performed.

Returning briefly to 230, a bin of the illustrated histogram may be seenas varying in size. In an implementation, variance in histogram bin sizemay represent variations in measurement density for a nearby grid point.In one possible example, a larger bin may represent a correspondinglylarge number of labeled measurements near a popular attraction of NewYork, (e.g., Statue of Liberty). In this instance, a larger bin mayindicate that a large number of labeled measurements corresponding toimages captured at the Statue of Liberty may be represented as inputsignals to process embodiment 20.

In an illustrated histogram of 230, smaller bins, for example, mayindicate that a lesser number of labeled measurements may be representedas input signals to process embodiment 20. For example, it may bereasonable to assume that somewhat fewer images may be captured at lesspopular locations within New York City, such as at locations along thebanks of the East River. Likewise, bins of a histogram that appear to beblank or unpopulated may be indicative of locations at that do notcorrespond to locations at which any captured images are represented asinputs signals to process embodiment 20. It should be noted, however,that in other implementations, histogram bins represented at 230 may beconstructed differently, such as by way of a grayscale, wherein darkershades of gray indicate higher density, and lighter shades of grayindicate lower density, as an example.

In an implementation, for a layer of higher resolution, computation ofkernel functions for Gaussian smoothing, such as at 235, might not beperformed and smoothing might not be used. Thus, while displaying adistribution of locations corresponding to labeled measurements, forexample, an unfiltered or “raw” distribution of discretized locationsmay be displayed. Accordingly, at 240, it may be seen that for a layert=0.0, at least in the facet shown in FIG. 2, discrete points of varyingsizes may be displayed. Discrete points of varying sizes may, forexample, indicate variations in density across a chosen axis, such asthe x-axis (e.g. latitude) or the y-axis (e.g., longitude), for example.It should be noted that in other implementations, some level ofsmoothing may occur at layer t=0.0, and claimed subject matter is notlimited in this respect.

In an embodiment, at subsequent layers, for example, based at least inpart on kernel functions calculated at 235, effects of Gaussiansmoothing across a chosen axis (such as longitude and/or latitude) maybe seen at 240. At a layer t=1.0, it may be seen at 240 that discretizedlocations of relatively high density may be represented as a linesegment that may result from a smoothing process. At layer t=2.0, it maybe seen that a line segment present at layer t=1.0 is replaced by moregradual transitions and/or softer curves for an embodiment. Likewise, ata layer t=n, gradual transitions and/or softer curves shown at layert=2.0 may be replaced by even more gradual transitions, reflecting anincreased level of Gaussian smoothing that may occur as scale isincreased, such as for an embodiment. However, again, it should be notedthat contours illustrated at 240 merely describe one illustrativeexample implementation, such as Gaussian smoothing in a singledimension, for longitude or latitude. For example, an embodiment mayinclude many dimensions, as previously mentioned.

In evaluating effects of smoothing at 240, similarities between 240 andFIGS. 1A-1C may be seen. At layer t=1.0, for example, relatively finefeatures of identified regions may be discernible. Thus, layer t=1.0 maycorrespond, at least in some implementations, to a one-dimensionalportion or “slice” of FIG. 1C, in which relative fine features ofidentified region 100″ may be distinguished, at a scale of 1.0 cm equalsapproximately 150.0 km, for example. Layer t=1.0 may thus correspond, atleast in one possible example, to an identified region displayinglocations of labeled measurements in relatively fine detail. However,again, commonality among features of 240 of FIG. 2 and FIGS. 1A-1C ismerely illustrative, and claimed subject matter is not limited in thisrespect.

At layer t=2.0 of block 240, for example, less relatively fine featuresof identified regions may be discernible. Thus, layer t=2.0 maycorrespond, at least in some implementations, to a one dimensionalportion of FIG. 1B, in which at least some features of identified region100′ may be distinguished, at a scale of 1.0 cm equals approximately300.0 km. Likewise, at layer t=n shown at 240, for example, few featuresof identified regions may be discernible. Thus, layer t=n may thuscorrespond, at least in an example, to an identified region displayinglocations of labeled measurements in relatively less detail, such asillustrated by region 100 of FIG. 1A, for example.

Returning to FIG. 2, at 245 conformance of at least one of Label_1 andLabel_2 may be determined. In implementations, detecting conformance ofa first label, such as Label_1, relative to a second label, such asLabel_2, may permit selective display of certain regions identified byparticular uniformity and/or conformance of labeled measurements. In onepossible example, to illustrate, a tourism official may be interested indetermining how many visitors to New York City actually capture imagesat the Statue of Liberty versus those who capture images at the EastRiver. To enable such an investigation, captured images corresponding tolocations within New York City may, for example, be assignedLabel_1=“Statue of Liberty.” Other captured images, for example, may beassigned Label_2=“East River.” Accordingly, by comparing conformance ofthe two labels (e.g., “Statue of Liberty” vs. “East River”) among anumber of labeled measurements may provide insights to such aninvestigation. Further, displaying identified regions characterized bylocations corresponding to captured images may, at least in part, allowvisualization of identified regions associated with prominent labels,for example. It should be noted, however, that this is merely oneexample of a myriad of examples in which labels may be assigned tomeasurements to give rise to useful insights, such as using conformanceand/or prominence among labels, as an example. Claimed subject matter isnot limited in this respect.

In some implementations, it may be useful to emphasize a contribution ofconforming labels in relation to a set comprising a larger group labels.For example, if 15 labels from a set of 100 labels are substantiallyidentical, this may be expressed as a conformance parameter of 15/100.However, it may be useful to additionally employ a “prominence”parameter, as an enhancement of a conformance parameter, in anembodiment. In one example, a prominence parameter may be described inmathematical terms, substantially in accordance with expression 3,below:

$\begin{matrix}\begin{matrix}{L_{\lambda}^{\prime} = \frac{\left( {L_{\lambda}\left( {w,{h;t}} \right)} \right)^{2}}{L_{\Lambda}\left( {w,{h;t}} \right)}} & (3)\end{matrix} & \;\end{matrix}$wherein, prominence for a label measure at a particular grid point ofhistogram 230, for example, is given by L′_(λ). L_(λ)(w,h;t) expressesincidence of a particular label at a particular grid point, andL_(Λ)(w,h;t) expresses a measure density at a particular grid pointacross labels, at least in one implementation. Of course, variable “t”represents a particular layer at which prominence is to be determined.In one possible example, a conformance parameter of 15/100 may beconverted, in accordance with expression 3, for example, to a prominenceparameter of 2.25. Accordingly, in one example, a 1.0% increase in thepresence of a label, such as from 15.0% to 16.0%, may result in anincrease in prominence of 2.25 to 2.56.

Conformance and/or prominence of labels associated with measurements maybe determined on a per-layer basis in an embodiment, for example. In animplementation, detecting conformance and/or prominence on a per-layerbasis may allow visualization of identified regions associated withparticular labels at levels of scale appropriate to a layer and allowdiscarding measured labels associated with less conforming and/or lessprominent labels, if desired. For example, a tourism official interestedin determining interests of visitors of New York City may wish to studylabel conformance and/or prominence of labeled measurements on a citylevel, and may not be interested in tourism happenings at a state level.Accordingly, at a city level, labels, such as, for example, “Statue ofLiberty” or other attractions may be of particular interest. In anotherexample, a tourism official interested in determining interests ofvisitors to New York State, may, for example, be interested in labelconformance of measurements at the state level, where prominent labelsas “Poconos Mountains,” or “Finger Lakes,” may be emphasized whiledeemphasizing and/or discarding measurements having less prominentlabels. However, other implementations may determine conformance and/orprominence based at least in part on criteria other than by a per layerbasis, and claimed subject matter is not limited in this respect.Rather, this is merely one illustrative example.

In an implementation, a prominence parameter may be computedsubstantially according to expression 3, wherein a particular label,such as L_(λ)(w, h; t) is squared (e.g., (L_(λ)(w, h; t))²) to emphasizeits contribution in relation to occurrence of other labels in a givenlayer (t), such as L_(Λ)(w,h;t). Although expression 3 indicates that anemphasis, such as prominence, may be computed using a square ofcontribution of a particular label, operations other than squaring thenumerator of expression 3 may be performed in other computations tononetheless provide a numerical measure of emphasis. For example, inlieu of squaring the numerator, the numerator may be raised to a powerof, for example, 1.5. In other implementations, exponents greater than2.0 may be employed, such as 2.1, 2.5, and so forth. Further, in someimplementations, variations of expression 3 may be employed in which thenumerator of expression 3 need not be exponentiated. Claimed subjectmatter is intended to embrace any mathematical operation to obtainemphasis of a first label relative to a second label, for example.

In an implementation, conformance and/or prominence may be used toidentify sub-regions within identified regions having particular levelsof conformance and/or prominence. For example, a first iso-contour mayidentify a sub-region within which a conformance parameter of at least15/100 with respect to a particular label may be present. A secondiso-contour, for example, may identify a sub-region within which aconformance parameter of at least 25/100 with respect to a particularlabel may be present. Likewise, iso-contours may identify sub-regionswithin which prominence parameters with respect to a particular label,such as greater than 2.25, greater than 2.56, and so forth, may bepresent.

Reference number 250 of FIG. 2 may include use of a spatial bandpassfilter that serves to filter or to at least limit a contribution ofspatial frequencies that may be inappropriate for a level of scale foran identified region being displayed. In this context, the term spatialfrequency refers to a spatial domain measure of irregularity and/orundulation in a number of occurrences. For example, in FIG. 1A, it canbe seen that relatively fine details present at, for example, FIG. 1B orFIG. 1C are removed or omitted. Thus, for example, in the event that aresearcher is interested in high-level trends, such as the occurrence ofdroughts across the continental United States, it may be appreciatedthat details presented pertain more to high-level contours rather thanto relatively fine details. Likewise, a researcher studying a particulardrought in the San Joaquin Valley of California (FIG. 1B), may beinterested in identified regions encompassed by a drought, and may beless interested in those locations within the San Joaquin Valleycorresponding to measurements labeled with “severe drought,” forexample.

In mathematical terms, at least for an implementation, spatial filteringmay be expressed in expression 4 substantially as follows:

$\begin{matrix}{{{Filter}\mspace{14mu}{output}} = \left\{ \begin{matrix}1 & {{{{{if}\mspace{14mu}{L_{\lambda}\left( {w,{h;t}} \right)}} - {L_{\lambda}\left( {w,{h;{t + a}}} \right)}} > ɛ}\mspace{14mu}} \\0 & {otherwise}\end{matrix} \right.} & (4)\end{matrix}$wherein L_(λ)(w, h; t) expresses occurrence of measurements associatedwith a particular label at a particular grid point (w, h) for a layer“t.” L_(λ)(w, h; t+a) expresses occurrence of measurements associatedwith a particular label at a particular point (w, h), at a slightlyhigher layer of t+a. A residual “E” indicates a measure of “visualnoise” and/or “jitter” that is to be tolerated.

In an implementation, selection of variable “a” used in a difference(e.g., bandpass filtering) operation, such as expression 4 may approachzero. In this instance, expression 4 may closely approximate theLaplacian of the Gaussian, which may effectively detect edges betweentwo areas of relatively uniform, but different, intensities in ahistogram, such as histogram 230, although, likewise, other methods mayalso be used. In some instances, variable “a” may comprise a somewhatlarger numeral, and claimed subject matter is intended to embraceimplementations in which “a” may approach zero as well as those forwhich “a” may approximate larger values. In implementations, forexample, layers t=4.0, t=16.0, t=64.0 and so forth may be used. Variable“a” may comprise values such as 5.0, 10.0, 15.0, or larger and/or may beexpressed as a function of t, for example.

At 255, one or more image processing techniques may be used to formboundaries of identified regions characterized by labeled measurements.In an implementation, use of image processing techniques correspondingto morphological transforms, such as filling, closing, contour tracing,dilation, erosion, and/or other operations, either alone or incombination, may provide visually meaningful shaping as well as removalof details that may not be appropriate or consistent with a given layer.In one example, returning briefly to FIGS. 1A-1C, identified 112, 114,116, and 118, visible in FIG. 1B, and which span and/or encompassregions on the order of 100.0 km, are not visible in identified region100 of FIG. 1A, for which 100.0 km may be approximately equal 1/10 of acentimeter. Thus, while viewing FIG. 1A, which may represent a layercorresponding to a scale having relatively low resolution among FIGS.1A-1C, a user need not be distracted by relatively fine details that maybe relatively uninteresting if viewed at a layer showing a continent,for example.

Image processing techniques, such as morphological filling, which may,for example, emphasize spatial extent of a label measure rather thandensity, may be achieved by contour tracing a binary representation toextract outer contours that may mark or designate areas where influenceof a label measure may drop below a threshold, for example. If outercontours of an identified region may be discerned, areas enclosed byouter contours may be filled, such as by identified region 100, as inFIG. 1A. Likewise, identified regions 113, 117, 115, and 119, forexample, are visible in FIG. 1C, but are filled by surroundingidentified regions 112′, 116′, 114′, and 118′, respectively, in FIG. 1B.However, operations and/or processing may also be achieved through othertechniques, and claimed subject matter is not limited in this respect.

Image processing techniques employed at 255 may also includemorphological closing. For example, returning to FIG. 1C, identifiedregions 126 may be seen as slightly detached from identified region116′. Similarly, identified region 127 may be seen as slightly detachedfrom identified region 100″. In at least certain implementations, detailmay be of a reduced relative signal value while identified region 100may be viewed with lower resolution, such as in FIG. 1B, to showidentified region 100′. To remove identified regions 126 and 127,morphological closing may be employed in which disjointed or detachedfeatures are separated from an identified region by a small gap, forexample, so as to close the gap. Morphological closing may beimplemented in an embodiment by performing a dilation operation followedby an erosion operation. In a dilation and/or erosion operation, adisk-shaped structuring element “S” may be used to form a binaryrepresentation of an identified region. Mathematically, this may beexpressed substantially in accordance with expression 5 below:b(w,h;t)·S=(b(w,h;t)⊕S)⊖S  (5)wherein b(w,h;t) of expression 5 denotes a binary representation of anidentified region, and wherein “S” denotes a disk-shaped structuringelement having a radius “ρ” selected substantially according to adesired smoothing, as illustrated in FIG. 3. In the example of FIG. 3,disk 310 having radius p may be selected substantially according to aparticular scale, as influenced by a particular layer, such as t=2.0,t=3.0, and so forth. In an implementation, for layers in which a greaterlevel of detail is desired, such as layer t=1.0, for example, a disk ofa smaller radius “ρ” may be appropriate, resulting in less smoothing ofan outer contour. For layers in which a lesser relative level of detailis desired, such as layer t=5.0, a disk of a larger radius “ρ” may beappropriate, resulting in greater relative smoothing of an outercontour.

FIGS. 4A and 4B show additional image processing techniques that may beused to assist in forming an identified region according to anembodiment. In FIG. 4A, embodiment 40 shows identified region 350 in adilated state, which may reduce relative detail in outer contours.Dilation of identified region 350 results, at least in part, in dilatedidentified region 350′. In FIG. 4B, dilated identified region 350′ iseroded to form identified region 350″, comprising much less relativedetail than identified region 350 shown in FIG. 4A.

Image processing techniques referenced in descriptions of FIGS. 3, 4A,and 4B represent just a few of many possible techniques that may beutilized to identify regions from a binary representation of anidentified region, such as given by b(w,h;t) of expression 5, in anembodiment. For example, a morphological closing operation, as describedwith reference to FIG. 3, may be performed by one or moredilation/erosion operations, such as described with reference to FIGS.4A, and 4B, for example. In another example, morphological filling, suchas previously described herein, may be substituted for, or performed inaddition to, a flood filling operation, that, for example, may skipinner (nested) contours, such as identified regions 113, 117, 115, and119 of FIG. 1C. It is contemplated that claimed subject matter embracesany of a host of possible image-processing techniques, such as, forexample, technique to process contours of identified region boundaries.

For purposes of illustration, FIG. 5 is an illustration of an embodimentof a computing platform or computing device 410 that may be employed ina client-server type interaction, such as described infra. In FIG. 5, aserver may interface with a client 400 or 410, which may comprisefeatures of a conventional client device, for example. Communicationsinterface 420, processor (e.g., processing unit) 450, and memory 470,which may comprise primary memory 474 and secondary memory 476, maycommunicate by way of communication bus 440, for example. In FIG. 5,client 410 may store various forms of content, such as analog,uncompressed digital, lossless compressed digital, or lossy compresseddigital formats for content of various types, such as video, imaging,text, audio, etc. in the form physical states or signals, for example.Client 410 may communicate with a server by way of an Internetconnection via network 415, for example. Although the computing platformof FIG. 5 shows the above-identified components, claimed subject matteris not limited to computing platforms having only these components asother implementations may include alternative arrangements that maycomprise additional components, fewer components, or components thatfunction differently while achieving similar results. Rather, examplesare provided merely as illustrations. It is not intended that claimedsubject matter to limited in scope to illustrative examples.

Processor 450 may be representative of one or more circuits, such asdigital circuits, to perform at least a portion of a computing procedureor process. By way of example but not limitation, processor 450 maycomprise one or more processors, such as controllers, microprocessors,microcontrollers, application specific integrated circuits, digitalsignal processors, programmable logic devices, field programmable gatearrays, and the like, or any combination thereof. In implementations,processor 450 may perform signal processing to manipulate signals orstates or to construct signals or states, for example.

Memory 470 may be representative of any storage mechanism. Memory 470may comprise, for example, primary memory 474 and secondary memory 476,additional memory circuits, mechanisms, or combinations thereof may beused. Memory 470 may comprise, for example, random access memory, readonly memory, or one or more data storage devices or systems, such as,for example, a disk drive, an optical disc drive, a tape drive, asolid-state memory drive, just to name a few examples. Memory 470 may beutilized to store a program, as an example. Memory 470 may also comprisea memory controller for accessing computer readable-medium 480 that maycarry and/or make accessible content, code, and/or instructions, forexample, executable by processor 450 or some other controller orprocessor capable of executing instructions, for example.

Under the direction of processor 450, memory, such as cells storingphysical states, representing for example, a program, may be executed byprocessor 450 and generated signals may be transmitted via a network,such as the Internet, for example. Processor 450 may also receivedigitally encoded signals from server 400.

Network 415 may comprise one or more communication links, processes,and/or resources to support exchanging communication signals between aclient and server, which may, for example, comprise one or more servers(not shown). By way of example, but not limitation, network 415 maycomprise wireless and/or wired communication links, telephone ortelecommunications systems, Wi-Fi networks, Wi-MAX networks, theInternet, the web, a local area network (LAN), a wide area network(WAN), or any combination thereof.

The term “computing platform,” as used herein, refers to a system and/ora device, such as a computing device, that includes a capability toprocess and/or store data in the form of signals and/or states. Thus, acomputing platform, in this context, may comprise hardware, software,firmware, or any combination thereof (other than software per se).Computing platform 410, as depicted in FIG. 5, is merely one suchexample, and the scope of claimed subject matter is not limited to thisparticular example. For one or more embodiments, a computing platformmay comprise any of a wide range of digital electronic devices,including, but not limited to, personal desktop or notebook computers,high-definition televisions, digital versatile disc (DVD) players and/orrecorders, game consoles, satellite television receivers, cellulartelephones, personal digital assistants, mobile audio and/or videoplayback and/or recording devices, or any combination of the above.Further, unless specifically stated otherwise, a process as describedherein, with reference to flow diagrams and/or otherwise, may also beexecuted and/or affected, in whole or in part, by a computing platform.

Memory 470 may store cookies relating to one or more users and may alsocomprise a computer-readable medium that may carry and/or makeaccessible content, code and/or instructions, for example, executable byprocessor 450 or some other controller or processor capable of executinginstructions, for example. A user may make use of an input device, suchas a computer mouse, stylus, track ball, keyboard, or any other devicecapable of receiving an input from a user.

Regarding aspects related to a communications or computing network, awireless network may couple client devices with a network. A wirelessnetwork may employ stand-alone ad-hoc networks, mesh networks, WirelessLAN (WLAN) networks, cellular networks, or the like. A wireless networkmay further include a system of terminals, gateways, routers, or thelike coupled by wireless radio links, and/or the like, which may movefreely, randomly or organize themselves arbitrarily, such that networktopology may change, at times even rapidly. Wireless network may furtheremploy a plurality of network access technologies, including Long TermEvolution (LTE), WLAN, Wireless Router (WR) mesh, or 2nd, 3rd, or 4thgeneration (2G, 3G, or 4G) cellular technology, or other technologies,or the like. Network access technologies may enable wide area coveragefor devices, such as client devices with varying degrees of mobility,for example.

A network may enable radio frequency or wireless type communications viaa network access technology, such as Global System for Mobilecommunication (GSM), Universal Mobile Telecommunications System (UMTS),General Packet Radio Services (GPRS), Enhanced Data GSM Environment(EDGE), 3GPP Long Term Evolution (LTE), LTE Advanced, Wideband CodeDivision Multiple Access (WCDMA), Bluetooth, 802.11b/g/n, or other, orthe like. A wireless network may include virtually any type of nowknown, or to be developed, wireless communication mechanism by whichsignals may be communicated between devices, such as a client device ora computing device, between or within a network, or the like.

Communications between a computing device and a wireless network may bein accordance with known, or to be developed cellular telephonecommunication network protocols including, for example, global systemfor mobile communications (GSM), enhanced data rate for GSM evolution(EDGE), and worldwide interoperability for microwave access (WiMAX). Acomputing device may also have a subscriber identity module (SIM) card,which, for example, may comprise a detachable smart card that storessubscription information of a user, and may also store a contact list ofthe user. A user may own the computing device or may otherwise be itsprimary user, for example. A computing device may be assigned an addressby a wireless or wired telephony network operator, or an InternetService Provider (ISP). For example, an address may comprise a domesticor international telephone number, an Internet Protocol (IP) address,and/or one or more other identifiers. In other embodiments, acommunication network may be embodied as a wired network, wirelessnetwork, or combination thereof.

A computing device may vary in terms of capabilities or features.Claimed subject matter is intended to cover a wide range of potentialvariations. For example, a network device may include a numeric keypador other display of limited functionality, such as a monochrome liquidcrystal display (LCD) for displaying text. In contrast, however, asanother example, a web-enabled computing device may include a physicalor a virtual keyboard, mass storage, one or more accelerometers, one ormore gyroscopes, global positioning system (GPS) or otherlocation-identifying type capability, and/or a display with a higherdegree of functionality, such as a touch-sensitive color 2D or 3Ddisplay, for example.

A computing device may include or may execute a variety of now known, orto be developed operating systems, or derivatives and/or versions,including personal computer operating systems, such as a Windows, iOS orLinux, or a mobile operating system, such as iOS, Android, or WindowsMobile, or the like. A computing device may include or may execute avariety of possible applications, such as a client software applicationenabling communication with other devices, such as communicating one ormore messages, such as via email, short message service (SMS), ormultimedia message service (MMS), including via a network, such as asocial network including, but not limited to, Facebook, LinkedIn,Twitter, Flickr, or Google+, to provide only a few examples. A computingdevice may also include or execute a software application to communicatecontent, such as, for example, textual content, multimedia content, orthe like. A computing device may also include or execute a softwareapplication to perform a variety of possible tasks, such as browsing,searching, playing various forms of content, including locally stored orstreamed video, or games such as, but not limited to, fantasy sportsleagues. The foregoing is provided merely to illustrate that claimedsubject matter is intended to include a wide range of possible featuresor capabilities.

A network including a computing device, for example, and may also beextended to another device communicating as part of another network,such as via a virtual private network (VPN). To support a VPN,transmissions may be forwarded to the VPN device. For example, asoftware tunnel may be created. Tunneled traffic may, or may not beencrypted, and a tunneling protocol may be substantially complaint withor substantially compatible with any past, present or future versions ofany of the following protocols: IPSec, Transport Layer Security,Datagram Transport Layer Security, Microsoft Point-to-Point Encryption,Microsoft's Secure Socket Tunneling Protocol, Multipath Virtual PrivateNetwork, Secure Shell VPN, or another existing protocol, or anotherprotocol that may be developed.

A network may be compatible with now known, or to be developed, past,present, or future versions of any, but not limited to the followingnetwork protocol stacks: ARCNET, AppleTalk, ATM, Bluetooth, DECnet,Ethernet, FDDI, Frame Relay, HIPPI, IEEE 1394, IEEE 802.11, IEEE-488,Internet Protocol Suite, IPX, Myrinet, OSI Protocol Suite, QsNet,RS-232, SPX, System Network Architecture, Token Ring, USB, or X.25. Anetwork may employ, for example, TCP/IP, UDP, DECnet, NetBEUI, IPX,Appletalk, other, or the like. Versions of the Internet Protocol (IP)may include IPv4, IPv6, other, and/or the like.

It will, of course, be understood that, although particular embodimentswill be described, claimed subject matter is not limited in scope to aparticular embodiment or implementation. For example, one embodiment maybe in hardware, such as implemented to operate on a device orcombination of devices, for example, whereas another embodiment may bein software. Likewise, an embodiment may be implemented in firmware, oras any combination of hardware, software, and/or firmware, for example(other than software per se). Likewise, although claimed subject matteris not limited in scope in this respect, one embodiment may comprise oneor more articles, such as a storage medium or storage media. Storagemedia, such as, one or more CD-ROMs and/or disks, for example, may havestored thereon instructions, executable by a system, such as a computersystem, computing platform, or other system, for example, that mayresult in an embodiment of a method in accordance with claimed subjectmatter being executed, such as a previously described embodiment, forexample; although, of course, claimed subject matter is not limited topreviously described embodiments. As one potential example, a computingplatform may include one or more processing units or processors, one ormore devices capable of inputting/outputting, such as a display, akeyboard and/or a mouse, and/or one or more memories, such as staticrandom access memory, dynamic random access memory, flash memory, and/ora hard drive.

In the preceding detailed description, numerous specific details havebeen set forth to provide a thorough understanding of claimed subjectmatter. However, it will be understood by those skilled in the art thatclaimed subject matter may be practiced without these specific details.In other instances, methods and/or apparatuses that would be known byone of ordinary skill have not been described in detail so as not toobscure claimed subject matter. Some portions of the preceding detaileddescription have been presented in terms of logic, algorithms, and/orsymbolic representations of operations on binary signals or states, suchas stored within a memory of a specific apparatus or special purposecomputing device or platform. In the context of this particularspecification, the term specific apparatus or the like includes ageneral-purpose computing device, such as general-purpose computer, onceit is programmed to perform particular functions pursuant toinstructions from program software.

Algorithmic descriptions and/or symbolic representations are examples oftechniques used by those of ordinary skill in the signal processingand/or related arts to convey the substance of their work to othersskilled in the art. An algorithm is here, and generally, is consideredto be a self-consistent sequence of operations and/or similar signalprocessing leading to a desired result. In this context, operationsand/or processing involves physical manipulation of physical quantities.Typically, although not necessarily, such quantities may take the formof electrical and/or magnetic signals and/or states capable of beingstored, transferred, combined, compared, processed or otherwisemanipulated as electronic signals and/or states representinginformation. It has proven convenient at times, principally for reasonsof common usage, to refer to such signals and/or states as bits, data,values, elements, symbols, characters, terms, numbers, numerals,information, and/or the like. It should be understood, however, that allof these or similar terms are to be associated with appropriate physicalquantities and are merely convenient labels. Unless specifically statedotherwise, as apparent from the following discussion, it is appreciatedthat throughout this specification discussions utilizing terms such as“processing,” “computing,” “calculating,” “determining”, “establishing”,“obtaining”, “identifying”, “selecting”, “generating”, and/or the likemay refer to actions and/or processes of a specific apparatus, such as aspecial purpose computer and/or a similar special purpose computingdevice. In the context of this specification, therefore, a specialpurpose computer and/or a similar special purpose computing device iscapable of processing, manipulating and/or transforming signals and/orstates, typically represented as physical electronic and/or magneticquantities within memories, registers, and/or other information storagedevices, transmission devices, and/or display devices of the specialpurpose computer and/or similar special purpose computing device. In thecontext of this particular patent application, as mentioned, the term“specific apparatus” may include a general-purpose computing device,such as a general-purpose computer, once it is programmed to performparticular functions pursuant to instructions from program software.

In some circumstances, operation of a memory device, such as a change instate from a binary one to a binary zero or vice-versa, for example, maycomprise a transformation, such as a physical transformation. Withparticular types of memory devices, such a physical transformation maycomprise a physical transformation of an article to a different state orthing. For example, but without limitation, for some types of memorydevices, a change in state may involve an accumulation and/or storage ofcharge or a release of stored charge. Likewise, in other memory devices,a change of state may comprise a physical change, such as atransformation in magnetic orientation and/or a physical change ortransformation in molecular structure, such as from crystalline toamorphous or vice-versa. In still other memory devices, a change inphysical state may involve quantum mechanical phenomena, such as,superposition, entanglement, and/or the like, which may involve quantumbits (qubits), for example. The foregoing is not intended to be anexhaustive list of all examples in which a change in state form a binaryone to a binary zero or vice-versa in a memory device may comprise atransformation, such as a physical transformation. Rather, the foregoingis intended as illustrative examples.

While there has been illustrated and/or described what are presentlyconsidered to be example features, it will be understood by thoseskilled in the relevant art that various other modifications may be madeand/or equivalents may be substituted, without departing from claimedsubject matter. Additionally, many modifications may be made to adapt aparticular situation to the teachings of claimed subject matter withoutdeparting from one or more central concept(s) described herein.Therefore, it is intended that claimed subject matter not be limited tothe particular examples disclosed, but that such claimed subject mattermay also include all aspects falling within appended claims and/orequivalents thereof.

The invention claimed is:
 1. An apparatus, comprising: one or more processors, coupled to a memory, to: construct a variety of histograms from a set of labeled measurements at a corresponding variety of scale levels; and smooth at least some of the variety of histograms, the smoothing to be increased responsive to a command from a user interface to decrease resolution of a displayed map to be generated from the variety of histograms.
 2. The apparatus of claim 1, wherein the one or more processors is additionally to: decrease smoothing of at least one of the variety of histograms responsive to a command from the user interface to increase resolution of the displayed map to be generated from the variety of histograms.
 3. The apparatus of claim 1, wherein the one or more processors is additionally to: identify at least one label region boundary at the variety of scale levels.
 4. The apparatus of claim 3, wherein the identifying of at least one label region boundary to comprise an identification of one or more sub-regions to be characterized by prominent labels of the set of labeled measurements.
 5. The apparatus of claim 3, wherein the one or more processors is additionally to: employ an image processing technique to assist in a formation of the at least one label region boundary, the image processing technique to comprise one or more of the following: filling, closing, contour tracing, dilation, erosion, or any combination thereof.
 6. The apparatus of claim 1, wherein the one or more processors is additionally to: smooth at least some of the variety of histograms to identify iso-contours at the variety of scale levels.
 7. The apparatus of claim 6, wherein to identify the iso-contours at the variety of scale levels is at least partially in response to a construction of the variety of histograms from the set of labeled measurements.
 8. The apparatus of claim 1, wherein to smooth the at least some of the variety of histograms to comprise: utilization of a Gaussian function to have a kernel size consistent with the set of labeled measurements at the variety of scale levels.
 9. The apparatus of claim 1, wherein the set of labeled measurements comprise labels to be assigned to captured images.
 10. The apparatus of claim 1, wherein to smooth the at least some of the variety of histograms to comprise utilization of a spatial bandpass filter to filter spatial frequencies between at least two scale levels of the variety of scale levels.
 11. The apparatus of claim 10, wherein the utilization of the spatial bandpass filter to scale the set of labeled measurements from a current to a different level in response to a receipt of a zoom command from the user interface.
 12. A method comprising: constructing a variety of histograms from a set of labeled measurements at a corresponding variety of scale levels; and smoothing at least some of the variety of histograms, wherein smoothing is decreased responsive to a command from a user interface to increase resolution of a displayed map generated from the variety of histograms.
 13. The method of claim 12, further comprising: increasing smoothing responsive to a command from the user interface to decrease resolution of the displayed map generated from the variety of histograms.
 14. The method of claim 12, further comprising: identifying at least one label region boundary at the variety of scale levels.
 15. The method of claim 14, wherein the identifying of at least one label region boundary comprises identifying one or more label region boundaries of sub-regions characterized by prominent labels of the set of labeled measurements.
 16. The method of claim 14, further comprising: employing an image processing technique to assist in forming the at least one label region boundary, the image processing technique comprising one or more of the following: filling, closing, contour tracing, dilation, erosion, or any combination thereof.
 17. The method of claim 12, further comprising smoothing at least some of the variety of histograms to identify iso-contours at the variety of scale levels.
 18. An apparatus, comprising: means for constructing a variety of histograms from a set of labeled measurements at a corresponding variety of scale levels; means for smoothing at least some of the variety of histograms, the smoothing to be increased responsive to a command from a user interface to decrease resolution of a map generated from the variety of histograms; and means for displaying the map generated from the variety of histograms.
 19. The apparatus of claim 18, further comprising: means for employing an image processing technique to assist in forming at least one label region boundary displayed by the means for displaying the map generated from the variety of histograms, the image processing technique comprising one or more of the following: filling, closing, contour tracing, dilation, erosion, or any combination thereof.
 20. The apparatus of claim 19, wherein the means for employing an image processing technique comprises means for employing a Gaussian function having a kernel size consistent with the set of labeled measurements at the variety of scale levels. 